kNet | level networking protocol library | Build Tool library

I have started using Julia.I read that it is faster than C. So far I have seen some libraries like KNET and Flux, but both are for Deep Learning. also there is a command "Pycall" tu use Python inside Julia.

But I am interested in Machine Learning too. So I would like to use SVM, Random Forest, KNN, XGBoost, etc but in Julia.

Is there a native library written in Julia for Machine Learning?

Thank you

I use Tensorflow for deep learning work, but I was interested in some of the features of Julia for ML. Now in Tensorflow, there is a clear standard that protocol buffers--meaning TFRecords format is the best way to load sizable datasets to the GPUs for model training. I have been reading the Flux, KNET, documentation as well as other forum posts looking to see if there is any particular recommendation on the most efficient data format. But I have not found one.

My question is, is there a recommended data format for the Julia ML libraries to facilitate training? In other words, are there any clear dataset formats that I should avoid because of bad performance?

Now, I know that there is a Protobuf.jl library so users can still use protocol buffers. I was planning to use protocol buffers for now, since I can then use the same data format for Tensorflow and Julia. However, I also found this interesting Reddit post about how the user is not using protocol buffers and just using straight Julia Vectors.

https://www.reddit.com/r/MachineLearning/comments/994dl7/d_hows_julia_language_mit_for_ml/

I get that the Julia ML libraries are likely data storage format agnostic. Meaning that no matter what format in which the data is stored, the data gets decoded to some sort of vector or matrix format anyway. So in that case I can use whatever format. But just wanted to make sure I did not miss anything in the documentation or such about problems or low performance due to using the wrong data storage format.

I am trying to convert Unirest

i want to be able to deal with response i'm receiving when i call the post method, i tested the post method in postman and it works fine and returns the following:

I am trying to create a network in tensor flow with multiple softmax outputs, each of a different size. The network architecture is: Input -> LSTM -> Dropout. Then I have 2 softmax layers: Softmax of 10 outputs and Softmax of 20 Outputs. The reason for this is because I want to generate two sets of outputs (10 and 20), and then combine them to produce a final output. I'm not sure how to do this in Tensorflow.

Previously, to make a network like described, but with one softmax, I think I can do something like this.